A Mean-Field-Game-Integrated MPC-QP Framework for Collision-Free Multi-Vehicle Control
In recent years, rapid progress in autonomous driving has been achieved through advances in sensing, control, and earning. However, as the complexity of traffic scenarios increases, ensuring safe interaction among vehicles remains a formidable challenge. Recent works combining artificial potential f...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/5/375 |
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| author | Liancheng Zheng Xuemei Wang Feng Li Zebing Mao Zhen Tian Yanhong Peng Fujiang Yuan Chunhong Yuan |
| author_facet | Liancheng Zheng Xuemei Wang Feng Li Zebing Mao Zhen Tian Yanhong Peng Fujiang Yuan Chunhong Yuan |
| author_sort | Liancheng Zheng |
| collection | DOAJ |
| description | In recent years, rapid progress in autonomous driving has been achieved through advances in sensing, control, and earning. However, as the complexity of traffic scenarios increases, ensuring safe interaction among vehicles remains a formidable challenge. Recent works combining artificial potential fields (APFs) with game-theoretic methods have shown promise in modeling vehicle interactions and avoiding collisions. However, these approaches often suffer from overly conservative decisions or fail to capture the nonlinear dynamics of real-world driving. To address these imitations, we propose a novel framework that integrates mean field game (MFG) theory with model predictive control (MPC) and quadratic programming (QP). Our approach everages the aggregate behavior of surrounding vehicles to predict interactive effects and embeds these predictions into an MPC-QP scheme for real-time control. Simulation results in complex driving scenarios demonstrate that our method achieves multiple autonomous driving tasks while ensuring collision-free operation. Furthermore, the proposed framework outperforms popular game-based benchmarks in terms of achieving driving tasks and producing fewer collisions. |
| format | Article |
| id | doaj-art-e3f07b1272ac49488340fd39f400ddf8 |
| institution | OA Journals |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-e3f07b1272ac49488340fd39f400ddf82025-08-20T01:56:16ZengMDPI AGDrones2504-446X2025-05-019537510.3390/drones9050375A Mean-Field-Game-Integrated MPC-QP Framework for Collision-Free Multi-Vehicle ControlLiancheng Zheng0Xuemei Wang1Feng Li2Zebing Mao3Zhen Tian4Yanhong Peng5Fujiang Yuan6Chunhong Yuan7School of Mechanical Engineering, Shandong Huayu University of Technology, Dezhou 253034, ChinaSchool of Information Engineering, Shandong Huayu University of Technology, Dezhou 253034, ChinaSchool of Mechanical Engineering, Shandong Huayu University of Technology, Dezhou 253034, ChinaDepartment of Engineering Science and Mechanics, Shibaura Institute of Technology, 3-7-5 Toyosu, Koto-ku, Tokyo 135-8548, JapanJames Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UKCollege of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, ChinaCollege of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, ChinaLaboratory of Intelligent Home Appliances, Department of Artificial Intelligence, College of Science and Technology, Ningbo University, Ningbo 315300, ChinaIn recent years, rapid progress in autonomous driving has been achieved through advances in sensing, control, and earning. However, as the complexity of traffic scenarios increases, ensuring safe interaction among vehicles remains a formidable challenge. Recent works combining artificial potential fields (APFs) with game-theoretic methods have shown promise in modeling vehicle interactions and avoiding collisions. However, these approaches often suffer from overly conservative decisions or fail to capture the nonlinear dynamics of real-world driving. To address these imitations, we propose a novel framework that integrates mean field game (MFG) theory with model predictive control (MPC) and quadratic programming (QP). Our approach everages the aggregate behavior of surrounding vehicles to predict interactive effects and embeds these predictions into an MPC-QP scheme for real-time control. Simulation results in complex driving scenarios demonstrate that our method achieves multiple autonomous driving tasks while ensuring collision-free operation. Furthermore, the proposed framework outperforms popular game-based benchmarks in terms of achieving driving tasks and producing fewer collisions.https://www.mdpi.com/2504-446X/9/5/375autonomous vehicleinteractive drivingrisk potential fieldmodel predictive control |
| spellingShingle | Liancheng Zheng Xuemei Wang Feng Li Zebing Mao Zhen Tian Yanhong Peng Fujiang Yuan Chunhong Yuan A Mean-Field-Game-Integrated MPC-QP Framework for Collision-Free Multi-Vehicle Control Drones autonomous vehicle interactive driving risk potential field model predictive control |
| title | A Mean-Field-Game-Integrated MPC-QP Framework for Collision-Free Multi-Vehicle Control |
| title_full | A Mean-Field-Game-Integrated MPC-QP Framework for Collision-Free Multi-Vehicle Control |
| title_fullStr | A Mean-Field-Game-Integrated MPC-QP Framework for Collision-Free Multi-Vehicle Control |
| title_full_unstemmed | A Mean-Field-Game-Integrated MPC-QP Framework for Collision-Free Multi-Vehicle Control |
| title_short | A Mean-Field-Game-Integrated MPC-QP Framework for Collision-Free Multi-Vehicle Control |
| title_sort | mean field game integrated mpc qp framework for collision free multi vehicle control |
| topic | autonomous vehicle interactive driving risk potential field model predictive control |
| url | https://www.mdpi.com/2504-446X/9/5/375 |
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